




'''simple docstring'''


import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				int=()					,       a_						:				Optional[Any]=None					,       a_						:				List[Any]="no"					,       a_						:				List[Any]="29500"      ):
  __a								=       False
  __a								=       False
  if any(key.startswith('KAGGLE'      ) for key in os.environ.keys()      ):
    __a								=       True
  elif "IPython" in sys.modules:
    __a								=       'google.colab' in str(sys.modules['IPython'].get_ipython()      )

  try:
    __a								=       PrecisionType(mixed_precision.lower()      )
  except ValueError:
    raise ValueError(
        f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."      )

  if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME'					,       a_      ) is not None):
    # TPU launch
    import torch_xla.distributed.xla_multiprocessing as xmp

    if len(AcceleratorState._shared_state      ) > 0:
      raise ValueError(
          'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
          'your training function. Restart your notebook and make sure no cells initializes an '
          '`Accelerator`.'      )
    if num_processes is None:
      __a								=       8

    __a								=       PrepareForLaunch(a_					,       distributed_type='TPU'      )
    print(f"Launching a training on {num_processes} TPU cores."      )
    xmp.spawn(a_					,       args=a_					,       nprocs=a_					,       start_method='fork'      )
  elif in_colab:
    # No need for a distributed launch otherwise as it's either CPU or one GPU.
    if torch.cuda.is_available():
      print('Launching training on one GPU.'      )
    else:
      print('Launching training on one CPU.'      )
    function(*a_      )
  else:
    if num_processes is None:
      raise ValueError(
          'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'      )

    if num_processes > 1:
      # Multi-GPU launch
      from torch.multiprocessing import start_processes
      from torch.multiprocessing.spawn import ProcessRaisedException

      if len(AcceleratorState._shared_state      ) > 0:
        raise ValueError(
            'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
            'inside your training function. Restart your notebook and make sure no cells initializes an '
            '`Accelerator`.'      )

      if torch.cuda.is_initialized():
        raise ValueError(
            'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
            'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
            'function.'      )

      # torch.distributed will expect a few environment variable to be here. We set the ones common to each
      # process here (the other ones will be set be the launcher).
      with patch_environment(
          world_size=a_					,       master_addr='127.0.01'					,       master_port=a_					,       mixed_precision=a_      ):
        __a								=       PrepareForLaunch(a_					,       distributed_type='MULTI_GPU'      )
        print(f"Launching training on {num_processes} GPUs."      )
        try:
          start_processes(a_					,       args=a_					,       nprocs=a_					,       start_method='fork'      )
        except ProcessRaisedException as e:
          if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
            raise RuntimeError(
                'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
                'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
                'Please review your imports and test them when running the `notebook_launcher()` to identify '
                'which one is problematic.'      ) from e

    else:
      # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
      if is_mps_available():
        __a								=       '1'
        print('Launching training on MPS.'      )
      elif torch.cuda.is_available():
        print('Launching training on one GPU.'      )
      else:
        print('Launching training on CPU.'      )
      function(*a_      )


def     SCREAMING_SNAKE_CASE							(   a_						:				Dict					,       a_						:				List[str]=()					,       a_						:				str=2      ):
  from torch.multiprocessing import start_processes

  with tempfile.NamedTemporaryFile() as tmp_file:
    # torch.distributed will expect a few environment variable to be here. We set the ones common to each
    # process here (the other ones will be set be the launcher).
    with patch_environment(
        world_size=a_					,       master_addr='127.0.01'					,       master_port='29500'					,       accelerate_mixed_precision='no'					,       accelerate_debug_rdv_file=tmp_file.name					,       accelerate_use_cpu='yes'					,       ):
      __a								=       PrepareForLaunch(a_					,       debug=a_      )
      start_processes(a_					,       args=a_					,       nprocs=a_					,       start_method='fork'      )









'''simple docstring'''


class  __lowercase    :  # Public class to implement a graph







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     __a								=       row
     __a								=       col
     __a								=       graph







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     bool:
     return (
         0 <= i < self.ROW
         and 0 <= j < self.COL
         and not visited[i][j]
         and self.graph[i][j]
     )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     # Checking all 8 elements surrounding nth element
     __a								=       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
     __a								=       [-1, 0, 1, -1, 1, -1, 0, 1]
     __a								=       True  # Make those cells visited
     for k in range(8   ):
       if self.is_safe(i + row_nbr[k]   ,				j + col_nbr[k]   ,				UpperCamelCase   ):
         self.diffs(i + row_nbr[k]   ,				j + col_nbr[k]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:  # And finally, count all islands.
     __a								=       [[False for j in range(self.COL   )] for i in range(self.ROW   )]
     __a								=       0
     for i in range(self.ROW   ):
       for j in range(self.COL   ):
         if visited[i][j] is False and self.graph[i][j] == 1:
           self.diffs(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
           count += 1
     return count



